University of Twente Student Theses
Anomaly detection in IoT network traffic
Wolters, Antoon (2023) Anomaly detection in IoT network traffic.
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| Abstract: | With the increasing usage of IoT systems in various sectors, ensuring the reliability and security of them has become a major priority. This paper investigates the effectiveness of certain Deep Learning and Machine Learning algorithms for anomaly detection in IoT network traffic. It aims to evaluate the performance of Machine Learning and Deep Learning algorithms in order to find the most optimal algorithm for a real-time use case. This conclusion is carefully made after analyzing the accuracy and performance on the IoT-23 and IoTID20 datasets for each of the following algorithms: XGBoost, Random Forest, Naïve Bayes, Decision Tree, Convolutional Neural Network, Stochastic Gradient Descent. The findings of this study can provide insight into the effectiveness of algorithms for IoT Security Systems. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/96125 |
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